Equity Expectations for AI Hires (VSOP/ESOP)
Equity remains an important part of AI hiring conversations, but the way it functions in practice has changed.
In 2025 and into 2026, successful teams are using equity less as a blunt incentive and more as a tool for aligning responsibility, risk, and long-term ownership.
Expectations typically differ by lane. For example, product-focused AI engineers tend to benchmark equity against senior software engineers, prioritising clear ownership, progression, and refresh mechanics over headline percentages.
Platform and MLOps profiles are typically more cash-sensitive, reflecting the operational burden and reliability expectations attached to these roles.
Research-to-production engineers often arrive with higher equity expectations shaped by lab and research environments, which can create friction when startups need near-term execution rather than open-ended exploration.
Across recent AI hiring mandates, equity expectations tend to cluster by company stage rather than by title:
Pre-seed to Seed: founding AI engineers typically expect equity in the 0.5–2.0% range, with higher allocations tied to broad scope, delivery ownership, and constrained cash compensation.
Series A: expectations compress to roughly 0.2–0.8%, as roles become more defined and early technical risk is reduced.
Series B and beyond: equity is usually below 0.3%, increasingly supplemented by refresh grants rather than upfront ownership.
We often see issues emerge when equity is used to compensate for unclear scope, inflated titles, or unresolved ownership. On the flip side, teams that navigate this well define role boundaries early, level AI roles consistently against the wider engineering organisation, and communicate how equity evolves as responsibility and impact grow.
This pressure is likely to increase as the EU Pay Transparency Directive comes into force. As salary ranges become more visible, vague or inflated AI titles will be harder to justify.
VSOP / ESOP: The Retention Lever Most Companies Undersell
In AI hiring, equity is frequently used as a differentiator, but we find that it’s rarely explained well enough to function as one.
Many candidates accept VSOP or ESOP grants without fully understanding dilution, vesting mechanics, refresh logic, or what different exit scenarios would actually mean in monetary terms. The percentage sounds compelling in an offer letter.
The real value is often abstracted, which becomes a retention risk.
If an engineer cannot clearly see how their equity grows, what performance unlocks look like, or how future funding rounds affect their stake, the grant loses motivational power. In uncertain liquidity markets, clarity matters more than headline generosity.
Strong operators now treat equity as an education process, not just a compensation component.
They explain total target value, realistic timelines, refresh expectations, and even downside scenarios.
But equity alone does not retain people. AI teams need environments where experimentation is safe. If engineers are afraid to fail, they will not innovate, and long-term incentives lose meaning.
You don’t need to hire the top five percent of global talent. You need to build a top five percent environment. In the right conditions, strong engineers become exceptional. In the wrong ones, even exceptional hires underperform.
AI salary premiums are visible to everyone, and mobile talent understands their value. Opaque equity structures create doubt, while transparent ones build trust.
The companies that retain the best are not those offering the largest grants, but those that make them understandable and achievable.
